EvolGAN Boosts Image Quality for Small or Difficult Datasets

Generative Adversarial Networks (GANs) are a model architecture for automatically discovering and learning the regularities or patterns in input data and using the learned patterns to generate or output new examples that plausibly could have been drawn from the original dataset. GANs are the current SOTA generative models in many domains -- most notably image synthesis and translation tasks. GAN models however require massive amounts of training data to reach decent performance. In an effort to make GANs more effective and reliable when only small, difficult, or multimodal datasets are available, a group of researchers from Facebook AI, University of the Littoral Opal Coast, University of Grenoble and University of Konstanz have proposed Evolutionary Generative Adversarial Networks (EvolGAN). The novel model uses a quality estimator and evolutionary optimization methods to search the latent space of generative adversarial networks trained on small or difficult datasets.